Building a Model with shiny and tinyAML

Steven Sanderson has a series on using the tidyAML Model Builder. Part 1 builds a simple model:

The first reactive expression, data, reads in the data file uploaded by the user or selects a built-in dataset, depending on which option the user chooses. If the user uploads a file, the read.csv() function is used to read the data file into a data frame. If the user selects a built-in dataset, the get() function is used to retrieve the data frame associated with that dataset. In both cases, the column names of the data frame are used to update the choices in the predictor_col select input, so that the user can select which column to use as the predictor variable.

Part 2 builds on it by adding new regression algorithms:

Yesterday I spoke about building tidymodels models using my package {tidyAML} and {shiny}. I have made an update to it, and will continue to make updates to it this week.

I have added all of the supported engines for regression problems only, NOT classification yet, that will be tomorrow’s work. I will then add a drop down for users to pick which backend function they want to use from {parsnp} like linear_reg().